Determining the accuracy in supervised fuzzy classification problems



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Gómez, Daniel and Montero, Javier (2008) Determining the accuracy in supervised fuzzy classification problems. In Computational intelligence in decision and control : proceedings of the 8th International FLINS Conference. World Scientific, Singapore, pp. 411-416. ISBN 978-981-279-946-3

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A large number of accuracy measures for image classification are actually available in the literature for cris classification. Overall accuracy, producer accuracy, user accuracy, kappa index and tau value are some examples. But in contrast to this effort in measuring the accuracy in a crisp framework, few proposals can be found in order to determine accuracy for soft classifiers. In this paper we define some accuracy measures for soft classification that extend some classical accuracy measures for crisp classifiers. This elms of measures takes into account the preferences of the decision maker in order to differentiate some errors that in practice may not be have same relevance.

Item Type:Book Section
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8th International Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science.
SEP 21-24, 2008

Uncontrolled Keywords:Kappa
Subjects:Sciences > Computer science > Programming languages (Electronic computers)
ID Code:16913
Deposited On:29 Oct 2012 11:01
Last Modified:19 Apr 2016 14:51

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